Computational information conservation theory (CICT): a natural framework for arbitrary-scale physical and biological system simulation

Human approach to experience is based on making decision in a natural uncertain environment by incomplete knowledge. Even Stochastic vs. Combinatorically Optimized Noise generation ambiguity emphasizes the major double-bind problem in current most advanced instrumentation systems, just at the inner core of human knowledge extraction by experimentation in science. To grasp a more reliable representation of reality and to get more effective physical and biological simulation techniques, researchers and scientists need two intelligently articulated hands: both stochastic and combinatorial approaches synergistically articulated by natural coupling. The first attempt to identify basic principles to get stronger simulation solution for scientific application has been developing at Politecnico di Milano University since the end of last century. The fundamental principles on computational information conservation theory (CICT), for arbitrary-scale system modeling and simulation from basic generator and relation through discrete paths denser and denser to one another, towards a never ending "blending quantum continuum," are recalled. Four examples are presented and discussed. This paper is a relevant contribute towards arbitrary-scale physical and biological systems modeling and simulation, to show how CICT can offer stronger and more effective system modeling algorithms for more reliable simulation.